{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123\n"
     ]
    }
   ],
   "source": [
    "print(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x265df76d688>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [i for i in range(10)]\n",
    "y = [j+2 for j in range(10)]\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x265df9c0c88>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline    # 由于 %matplotlib inline 的存在，当输入plt.plot(x,y_1)后，不必再输入 plt.show()，\n",
    "                      # 图像将自动显示出来   现在  不写魔法函数也能直接绘制了  \n",
    "import matplotlib\n",
    "\n",
    "#  nl  给代码标行数   mn  切换成markdown   y 切换到code\n",
    "x = [i for i in range(10)]\n",
    "y = [j+2 for j in range(10)]\n",
    "plt.plot(x,y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 编辑数学公式\n",
    "$E = mc^2$\n",
    "## 一元二次方程求解公式\n",
    "$$x = \\frac {(-b)\\pm \\sqrt{b^2-4ac}} {2a}$$\n",
    "\n",
    "$$ x = \\frac {4\\pm b^2}{5\\sum 5^3}$$"
   ]
  }
 ],
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